1. INTRODUCTION
Value can be demarcated as “the capacity of goods, services or activity to satisfy a need or provide a benefit to a person or legal entity” (Haksever et al., 2004). Various researchers have explained value co-creation in context to marketing, service, interaction, design, and innovation and new product development. The development of new products in a market is determined by recognizing the true requirements of customers and modifying existing products in accordance to the needs and requirements of customers (Bharti et al., 2014). To develop an innovative product, firms are working day and night in identifying innovative ideas. Therefore, engaging consumers in the process of product development is gaining popularity. Further, to gain competitive advantage, co-creation is emerging as a good idea and is also being considered as the foremost objective of companies. Co-creation has been defined as an approach to generate novice ideas for consumers and by consumers (Hoyer et al., 2010).
In other words, value co-creation is the experiential relationship between customer and firm which also affects consumer judgements about service delivery (Manser Payne et al., 2021), as the customer personally enhances the value of a product or service by being involved in and developing the entire service delivery process (Grönroos, 2012). This co-created value leads to higher customer engagement (Ramaswamy & Gouillart, 2010; van Doorn et al., 2010) through customer experiences (Ramaswamy, 2008). Yet, besides travelling (Smaliukiene et al., 2014) and scale validation (Albinsson et al., 2016; Taghizadeh et al., 2016), very little attention has been noticed in examining the intention to use e-resources among academicians and research scholars in higher education institutions.
Thus, this study represents one of the preliminary research endeavors, which shows the contribution of the DART model of value co-creation in developing e-resources. Previous studies have extended the DART model in various disciplines, like social media (Schiavone et al., 2014; Wan Ahmad et al., 2018), education (Fagerstrøm & Ghinea, 2013), government e-services (Adeleke & AbdulRahman, 2011), savings groups (Sithole et al., 2021), travel services (Smaliukiene et al., 2014), online retailing (Anshu et al., 2022), and multi-industry contexts (Oklevik et al., 2022). However, as far as researcher knowledge is concerned, none of the studies have employed this DART framework with respect to e-resources. To fulfil this gap, the current investigation aimed to examine the impact of value co-creation in evolving user satisfaction as well as the intention to adopt e-resources.
As far as the structure of the present research is concerned, it starts with an introduction by elaborating the significance of this study in the area of value co-creation and e-resources. Further, it extensively reviews and presents the literature on key concepts such as value co-creation, the DART model of value co-creation, and e-resources. The insights gathered from literature review guided in the formulation of research objectives and hypotheses. Further, this study moved toward drafting the research methodology to define the research design along with the instrument and process of data collection. Finally, the study ends with data analysis, discussion, and a conclusion. The uniqueness of the study lies in the last section of the study, which highlighted the implications for academicians, librarians, and e-resource providers. In addition, limitations and scope for future research have also been explicated.
2. REVIEW OF LITERATURE
2.1. Value Co-Creation
Co-creation can be defined as an “organization’s creativity or a form of commercial strategy, that brings different stakeholders together like ‘a company and a customer’ in order to cooperatively develop a product which satisfies customers as well as earns profits for the company”; Prahalad and Ramaswamy (2004b) further added in definition of co-creation that “it is the joint creation of value by the company and the customer; allowing the customer to co-construct the service experience to suit their context.” Moreover, in the process of co-creation, organizations leverage their own customers for product development (Saarijärvi, 2012). The voluntary involvement of customers in the businesses process, utilising either common or private resources, will ultimately produce significant consequences for the company, particularly having an impact on revenue (Krishna & Dhaka, 2013). In this process, value is jointly created by customers and service providers through engagement (Komulainen, 2014). Further, co-creation has been seen as an important antecedent in the service sector (Jaakkola et al., 2015) and is emerging as a forthcoming area of research (Cheung et al., 2021; Manser Payne et al., 2021; Nadeem et al., 2020; Ratten, 2022; Saha et al., 2020; Yen et al., 2020).
2.2. DART Model of Value Co-Creation
To understand variables such as satisfaction and intention to adopt e-resources, a DART model, put forth by Prahalad and Ramaswamy (2004a; 2004b), has been employed. This model elucidates the process of co-creation of values in terms of four types of company-customer communications (Schiavone et al., 2014). This model is found to be the best framework to understand and explain the values created by both customer and company (Shen et al., 2018). A meaningful dialogue requires the access of customers to the resources. Beyond these variables, an effective risk assessment is important for providing the information about risk associated with value co-creation. Similarly, transparency in sharing information with customers is also found necessary to add value (Albinsson et al., 2016), since the DART model’s fundamental tenet is predicated on the supposition that the market is made of certain co-created experiences which companies and customers share to develop new products and services (Schiavone et al., 2014). The concept of value co-creation has been emerged to deliver the same.
2.3. E-Resources
The usage and adoption of e-resources are increasing tremendously (Sivathaasan et al., 2014), and is expected to rise in the near future (Sampath Kumar & Kumar, 2010). One of the evident reasons for the popularity of e-resources is its up-to-date information delivery and anywhere 24/7 assess (Haridasan & Khan, 2009). E-resources mean information processing via electronic media in the form of e-books, online journals, or digital libraries (Sudhier & Seethalekshmi, 2011). According to Haridasan and Khan (2009), e-resources include different categories of online databases, e-journals, e-books, and Internet resources. Where academic journals are helpful in teaching and research works for academicians (Monopoli et al., 2002; Rani & Zainab, 2006), e-books and digital libraries are proved to be helpful for students (Fojtik, 2015; Morris & Lambe, 2017; Walton, 2014), and academicians both (Sampath Kumar & Kumar, 2010). It has been identified in a study that these teachers and students use e-resources either by a learning and doing method or after advice from their friends (Sampath Kumar & Kumar, 2010).
Further, the review of prior literature reveals that e-resource usage was an important research choice among scholars (Falloon & O’Reilly, 2020; Haridasan & Khan, 2009; Isibika & Kavishe, 2018; Monopoli et al., 2002; Sampath Kumar & Kumar, 2010; Sivathaasan et al., 2014; Sudhier & Seethalekshmi, 2011; Rani & Zainab, 2006; Tella et al., 2018). However, some scholars have utilized certain theories such as the “Diffusion of Innovation Theory” (Joshua & King, 2020), “unified theory of acceptance and use of technology” (Chang et al., 2015), and models like Performance Evaluation Model (Noh, 2012) in e-resource related research endeavour. Moreover, previous studies have examined the usage of electronic resources in libraries only and very less focus has been placed on employing components of value co-creation in developing e-resources for academicians and scholars.
3. OBJECTIVES AND HYPOTHESES FORMULATION
Two research objectives have been formulated with the help of previous research. These are:
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To identify the most influencing component of value co-creation in generating user satisfaction of e-resources.
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To investigate the impact of satisfaction on the user’s intention to use e-resources.
The following objectives of the study provided directions for drafting the hypotheses of the study:
3.1. Dialogue
Dialogue means communication, keen participation, and willingness to take action. Dialogue creates a loyal community by active learning and interaction, which is more than just listening to customers (Mohd Idros et al., 2018; Prahalad & Ramaswamy, 2004a; 2004b). Lusch and Vargo (2006) have defined dialogue as a conversation which helps both firm and customer to learn about the necessities and capabilities of each other. This active dialogue between the customer and provider creates value (Albinsson et al., 2016). Russo Spena et al. (2012) have defined dialogue as a three-way relationship, i.e., between firm and customer, among various customers, and between consumer and expert. It is said that the better the quality of dialogue, the better will be the customer co-created experience (Binkhorst & Den Dekker, 2009), and better customer experience will help in forming better customer satisfaction (Choi et al., 2013; Iglesias et al., 2019; Khan et al., 2015). Considering the findings of previous research, it is hypothesised that:
3.2. Access
Access has been described as the availability of information to customers that helps them in co-creation of value (Adeleke & AbdulRahman, 2011) by using accurate tools of communication (Mazur & Zaborek, 2014; Russo Spena et al., 2012). According to Ramaswamy (2005), “Accessibility of the consumers to service processes gives them an opportunity to be engaged in the design, development, setting price process and quality processes across the value network” (cf: Taghizadeh et al., 2016). Further it has been quoted that “In order to foster such a complex dialogue, a company must provide its customers with access to each other and to company listeners” (Ramaswamy, 2008). The access of a firm’s information resources increases the experience of customers (Solakis et al., 2017). Further, this experience influences them to facilitate the development of products and services (Prahalad & Ramaswamy, 2001). Although a handful of research efforts have revealed the positive role of customer experience in generating satisfaction among consumers (Choi et al., 2013; Iglesias et al., 2019; Khan et al., 2015), none of the research has cited the role of access in generating satisfaction and intention to adopt e-resources. Thus, we hypothesis that:
3.3. Risk Assessment
Risk assessment means the probability of risk associated with the product or service (Smaliukiene et al., 2014): “Risk assessment guidelines consider how to manage the risk/benefit proposition for both the customer and the company” (Ramaswamy, 2008). When the consumer acts as co-creator, he/she wishes to acquire additional information related to the potential risk linked with not only the development of products/services but also the consumption and distribution of the same (Ramaswamy, 2005). The customer accesses the risk information based on ranking, comments, reviews, etc. (Smaliukiene et al., 2014). The risk associated in services needs to be disclosed to help customers in order to facilitate their informed decisions (Adeleke & AbdulRahman, 2011). The informed decision will eventually create trust between company and customer (Prahalad & Ramaswamy, 2004c) that significantly contributes to the satisfaction of consumers (Park et al., 2017). Hence, it is posited that:
3.4. Transparency
Transparency means the openness of information regarding the products and technologies of the service provider (Smaliukiene et al., 2014). In the simplest and shortest manner, it can be comprehended as “shared information” (Ramaswamy, 2008). A well-informed consumer is considered to be more participative in the processes of the company (Prahalad & Ramaswamy, 2004c). These consumers have the freedom to reveal their feedback among the public (Smaliukiene et al., 2014). According to Adeleke and AbdulRahman (2011), transparency in services facilitates the co-creation of value. Thus, transparency is found to be an important variable in defining satisfaction. Prior studies have also supported the association between transparency and satisfaction and validate the role of transparency in evolving customer satisfaction (Eggert & Helm, 2003; Hegwer, 2015; Khosroshahi et al., 2019; Simintiras et al., 2015). Thus, the following hypothesis is proposed:
3.5. Satisfaction and Intention to Adopt
Satisfaction refers to “positive attitudes toward using the system” (Joo & Lee, 2011). Customer satisfaction facilitates service providers not only in building human behaviour but also in retention (Tussyadiah, 2016). Satisfaction has also been quoted as the strongest predictor for the continuance intentions of humans, essentially in the context of new technologies (Bhattacherjee, 2001). A satisfied customer exhibits a greater intention towards the products and services of a firm (Kim et al., 2009). Previous research as well elucidated an influential role of satisfaction in consumer adoption intention studies (Agrebi & Jallais, 2015; Belanche et al., 2012; Kumar et al., 2022; Revels et al., 2010). Thus, it is proposed:
4. RESEARCH METHODOLOGY
4.1. Research Design and Questionnaire
In the present study, a survey method was adopted to test the theoretical constructs. The data from respondents were gathered using a verified DART model scale. The measurement items of the DART model were taken from Taghizadeh et al. (2016) and Albinsson et al. (2016). A five-point Likert scale was used to determine the responses, categorising all the responses from strongly agree to strongly disagree. The Dialogue (6 items), and Risk Assessment and Transparency (5 items each) were adopted after modifications from Taghizadeh et al. (2016). Access (3 items) items were modified from Albinsson et al. (2016). Satisfaction (3 items) were adopted after modifications from the work of Joo and Lee (2011). Behavioral Intention (3 items) were taken from Fishbein and Ajzen (1975) and Shankar and Rishi (2020). Combining all these, a total of 25 items were taken for gathering data and further analysis. The questionnaire was administered in the English language and the pilot testing was performed on 20 respondents. To avoid repetition of results the pilot testing responses were not incorporated into the final sample.
4.2. Data Collection
The target population in the present study was Ph.D. scholars and faculty in the National Capital Region (NCR) of India. In line with previously published research (Madhusudhan, 2010), the present study adopted a descriptive research design by following two stage sampling. In the first stage, the top 50 business schools as per the National Ranking Framework (Ministry of Education) situated in the NCR (India) were chosen using judgemental sampling, based on the assumption that these institutes subscribe to the best e-resource platforms (Kumar et al., 2016; 2022). In the second stage, respondents were chosen using simple random sampling for data collection (Kumar et al., 2022). A total of 220 Ph.D. scholars and faculties, belonging to seven institutions situated in NCR, responded to the questionnaire. Table 1 describes the demographic information of the respondents. The data clearly shows that 65% of the responses were male. Most respondents (61.36%) were below the age of 35 years, 32.73% of the respondents were 35-45 years of age, and only 5.91% were from the age of 45 years and above. When respondents were asked about their familiarity towards e-resources, it was found that 66.82% of respondents were extremely familiar with e-resources, while 30.00% were moderately familiar, and only 3.18% mentioned themselves as less familiar.
Table 1
Demographic profile of respondents (n=220)
| Category | n (%) |
|---|---|
| Sex | |
| Male | 153 (69.55) |
| Female | 67 (30.45) |
| Age (yr) | |
| <35 | 135 (61.36) |
| 35-45 | 72 (32.73) |
| >45 | 13 (5.91) |
| Institutions | |
| Indian Institute of Technology, Delhi | 17 (7.72) |
| Management Development Institute, Gurugram | 19 (8.64) |
| Indian Institute of Foreign Trade, New Delhi | 28 (12.73) |
| Amity University, Gautam Budh Nagar, Noida | 37 (16.82) |
| Jamia Millia Islamia, New Delhi | 21 (9.54) |
| International Management Institute, New Delhi | 57 (25.91) |
| Institute of Management Technology, Ghaziabad | 41 (18.64) |
| Familiarity with e-resources | |
| Extremely familiar | 147 (66.82) |
| Moderately familiar | 66 (30.00) |
| Less familiar | 7 (3.18) |
5. DATA ANALYSIS AND RESULTS
Adanco 2.2 software (Informer Technologies, Inc.) was used for data analysis (Henseler & Dijkstra, 2015; Rasoolimanesh et al., 2019). For analysing the relationship among selected constructs, partial least square structural equation modelling has been applied. The data analysis has been performed and presented with the help of a measurement model and structural model (Henseler et al., 2009).
5.1. Measurement Model
Firstly, the measurement model was evaluated for testing the reliability and validity of the collected responses. The reliability was tested using construct and indicator reliability, whereas validity was checked through convergent and discriminant validity. The construct reliability was tested by composite reliability (CR) and Cronbach’s alpha value. As mentioned in Table 2, dialogue and transparency constructs scored Cronbach’s alpha value above 0.7, which is the minimum required value (Hair et al., 2010). However, access was able to meet the minimum threshold value (which is above 0.65) defined by Nunnally (1978). The values of CR were also calculated as above the threshold values 0.70 (Fornell & Larcker, 1981; Hair et al., 2010; 2018). These results have confirmed the reliability of the entire list of constructs. Further, the values of factor loadings were above the threshold value, i.e., 0.4 (Henseler et al., 2009) and 0.5 (Hair et al., 2010), confirming the reliability of all indicators. Adding to this, the convergent validity was analyzed using the average variance explained (AVE) which was found to be above the threshold value of 0.50 (Fornell & Larcker, 1981; Hair et al., 2010; 2018; Henseler et al., 2009).
Table 2
Summary of measurement model
| Construct | Item | Statement | AVE | CR | FL | Chronbach’s α |
|---|---|---|---|---|---|---|
| Dialogue | D1 | Use diversified communication channels to have dialogue sessions with e-resource users | 0.5114 | 0.8197 | 0.6506 | 0.8089 |
| D2 | Conduct dialogue session with e-resource users frequently | 0.6684 | ||||
| D3 | Involve internal parties during the dialogue session with e-resource users | 0.7381 | ||||
| D4 | Involve external parties during the dialogue session with e-resource users | 0.7777 | ||||
| D5 | Recognize the e-resource user’s experience regarding to e-resources | 0.6841 | ||||
| D6 | Emphasize the employees’ effort to individual e-resource users | 0.7622 | ||||
| Access | A1 | The e-resources websites lets the user decide how he/she receives the e-resource offering | 0.5895 | 0.7441 | 0.5711 | 0.6760 |
| A2 | The user has many options to choose how he/she experiences the e-resources service/product offering | 0.8643 | ||||
| A3 | It is easy for the user to receive the e-resource service/product offering when, where, and how he/she wants it | 0.8339 | ||||
| Risk assessment | R1 | Inform potential risks of the service product offered to e-resource users | 0.6875 | 0.9782 | 0.7848 | 0.8917 |
| R2 | Inform e-resource users about the limitation of the firm’s knowledge and capability | 0.8595 | ||||
| R3 | Recognize the changing dynamics of e-resource users’ needs | 0.9190 | ||||
| R4 | Accept e-resource users’ complaints on service product offerings | 0.8587 | ||||
| R5 | Shoulder all the risk-related responsibilities upon themselves | 0.7077 | ||||
| Transparency | T1 | Make clear to the users about the e-resource service product-related information | 0.5243 | 0.7688 | 0.7951 | 0.7704 |
| T2 | Disclose pricing related information to e-resource users | 0.7795 | ||||
| T3 | Get benefit from the information symmetry between users and the e-resources platform | 0.6210 | ||||
| T4 | Build trust among users through transparent information | 0.7106 | ||||
| T5 | Provide up-to-date information to users | 0.7009 | ||||
| Satisfaction | S1 | I am satisfied with this digital library overall | 0.6293 | 0.7184 | 0.7932 | 0.7052 |
| S2 | It is a pleasure to use this digital library to find what I want | 0.8487 | ||||
| S3 | I am comfortable and feel fulfilled while using this digital library | 0.7337 | ||||
| Intention to use e-resources |
IU1 | I intend to use e-resources in the future | 0.7662 | 0.8475 | 0.8782 | 0.8471 |
| IU2 | I expect that I will use e-resources in the future | 0.8491 | ||||
| IU3 | I plan to use e-resources in the future | 0.8979 |
Further, the heterotrait-monotrait ratio (HTMT) and Fornell-Larcker criterion were used to assess the discriminant validity of each construct (Table 3). Previous studies suggest that the value of HTMT above the threshold value reflects a lack of discriminant validity (Henseler et al., 2015). In present research the HTMT values were below the threshold value, i.e. 0.90, which proves the discriminant validity of the constructs (Gold et al., 2001; Hair et al., 2018). For the Fornell-Larcker criterion the square root of AVE needs to be greater than the correlation between the constructs (Fornell & Larcker, 1981). Since both the criteria fulfilled the requirement of discriminant validity conditions, it can be ascertained that discriminant validity was achieved. The values of variance inflation factor (VIF) were analyzed to evaluate the collinearity in the responses (Table 4). The result of the analysis shows the VIF values are falling within the range of 1.2 to 3.1 for all the items which are far below than benchmark value of 10, indicating no problem of multicollinearity (Hair et al., 2018).
Table 3
Discriminant validity
| Construct | Dialogue | Access | Risk-assessment | Transparency | Satisfaction | Intention to adopt |
|---|---|---|---|---|---|---|
| Heterotrait-monotrait ratio | ||||||
| Dialogue | ||||||
| Access | 0.7572 | |||||
| Risk-assessment | 0.0575 | 0.0095 | ||||
| Transparency | 0.8458 | 0.7139 | 0.0444 | |||
| Satisfaction | 0.8203 | 0.6050 | 0.0406 | 0.8859 | ||
| Intention to adopt | 0.7539 | 0.5654 | 0.0201 | 0.7720 | 0.8848 | |
| Fornell-Larcker criterion | ||||||
| Dialogue | 0.5114 | |||||
| Access | 0.3936 | 0.5895 | ||||
| Risk-assessment | 0.0029 | 0.0013 | 0.6875 | |||
| Transparency | 0.4500 | 0.3252 | 0.0011 | 0.5243 | ||
| Satisfaction | 0.4016 | 0.2286 | 0.0009 | 0.4387 | 0.6293 | |
| Intention to adopt | 0.4006 | 0.2449 | 0.0005 | 0.3889 | 0.4725 | 0.7662 |
Table 4
Variance inflation factor
| Indicator | Dialogue | Access | Risk-assessment | Transparency | Satisfaction | Intention to adopt |
|---|---|---|---|---|---|---|
| D1 | 1.4123 | |||||
| D2 | 1.5249 | |||||
| D3 | 1.5589 | |||||
| D4 | 1.8413 | |||||
| D5 | 1.4384 | |||||
| D6 | 1.6251 | |||||
| A1 | 1.2985 | |||||
| A2 | 1.5499 | |||||
| A3 | 1.2995 | |||||
| R1 | 2.8127 | |||||
| R2 | 3.1113 | |||||
| R3 | 2.9610 | |||||
| R4 | 2.2933 | |||||
| R5 | 1.5227 | |||||
| T1 | 1.7868 | |||||
| T2 | 1.6927 | |||||
| T3 | 1.1780 | |||||
| T4 | 1.4441 | |||||
| T5 | 1.5776 | |||||
| SS1 | 1.3676 | |||||
| SS2 | 1.5302 | |||||
| SS3 | 1.3220 | |||||
| II1 | 2.2065 | |||||
| II2 | 1.7935 | |||||
| II3 | 2.3910 |
The model fit was assessed with the help of standardized root mean square residual (SRMR) value, unweighted least squares discrepancy (dULS), and geodesic discrepancy (dG) values, using Adanco 2.2 software (Ashiru et al., 2022; Dijkstra & Henseler, 2015; Henseler, 2017; Henseler et al., 2016). However, most researchers consider SRMR as an approximate measure of model fit (Benitez-Amado et al., 2017; Hu & Bentler, 1998; Oh, 2022; Seetharaman et al., 2017) and dULS and dG values as overall model fit (Benitez-Amado et al., 2017). For the data to be fit with the model, the SRMR value needs to be under 0.08 (Henseler et al., 2016; Oh, 2022). Our estimated model fit value for SRMR was 0.0739, which falls below the cut-off value, thus representing a model fit (Table 5). Moreover, the values of dULS and dG are less than their corresponding HI95 and HI99 values, representing an overall model fit (Henseler, 2017; Henseler et al., 2016).
5.2. Structural Model
After achieving reliability and validity, the next step of analysis was to validate the structural model. Thus, to test the relationship between dependent and independent variables as proposed in the measurement model, the bootstrapping procedure using 4,999 subsamples was performed, which is also helpful in validating the theoretical model of the study (Hair et al., 2018).
The value of R2 represents the impact of independent variables on dependent ones. Analysed results show that 50.4% variation in user satisfaction has been observed due to all the variables of the DART model. A further 47.2% variation in behavioural intention to use e-resources has been noticed due to user satisfaction of e-resources (Table 6).
Table 6
Summary of results
| Effect | Hypothesis | Original coefficient |
Standard bootstrap results | |||
|---|---|---|---|---|---|---|
| Mean value | t-value | p-value (2-sided) | Results | |||
| Dialogue → Satisfaction | H1 | 0.3319 | 0.3204 | 3.3441 | 0.0008 | Supported |
| Access → Satisfaction | H2 | 0.0285 | 0.0313 | 0.4282 | 0.6685 | Not supported |
| Risk-assessment → Satisfaction | H3 | 0.0036 | -0.0002 | 0.0574 | 0.9542 | Not supported |
| Transparency → Satisfaction | H4 | 0.4236 | 0.4218 | 4.8546 | 0.0000 | Supported |
| Satisfaction → Intention to adopt | H5 | 0.6874 | 0.6663 | 6.6544 | 0.0000 | Supported |
The hypotheses related to DART and satisfaction H1 (path coefficient [PC]=0.332, p<0.01) and H4 (PC=0.424, p<0.01) were supported, showing a positive relation between dialogue to satisfaction and transparency to satisfaction, respectively. Transparency remained the influential factor, followed by dialogue, in achieving satisfaction, whereas H2 (PC=0.029, p>0.05) and H3 (PC=0.004, p>0.05) were not supported. This reveals that access and risk-assessment were not significant and are not positively related to satisfaction. Moreover, H5 was supported (PC=0.687, p<0.01), showing a positive relationship between the variables satisfaction and intention to adopt e-resources (Figs. 1 and 2).
6. DISCUSSION AND CONCLUSION
Co-creation has been described as a joint activity of the consumer and the organization which is performed for the purpose of adding value to the product or service. It has been reported that the level of brand satisfaction of users is high if they are involved in the co-creation process (Oklevik et al., 2022). Although value co-creation is being identified as an emerging area for research in marketing, service, innovation, and new product development related research, however, as per our knowledge, none of the referred studies have examined the role of value co-creation elements in generating satisfaction and intention to adopt e-resources in the academic community.
To overcome this limitation, the present study inspects the influence of value co-creation on satisfaction and intention to adopt e-resources. For this purpose, the DART model of value co-creation has been adopted. Four variables of the DART model, “dialogue, access, risk-assessment, and transparency” have been employed as independent variables for the study. On the other hand, satisfaction and intention to adopt e-resources played the role of dependent variable. For the purpose of collecting data, Ph.D. scholars and faculty members were selected to report their views on a structured questionnaire, using a 5-point Likert scale.
The conceptual framework was analyzed through structural equation modeling, on Adanco 2.2 software. The reliability of the scale was tested using construct and indicator reliability, whereas convergent and discriminant validity have been used to check the validity of the scale. The results of the study confirmed the reliability and validity of the scales for further investigation. Further, the fitness of the model was tested with the help of SRMR, dULS, and dG values, which have been found under the threshold limits, representing a perfectly fit model for analysis. Moreover, the path analysis section of structural equation modelling reveals that two out of four values positively influence satisfaction among the respondents. The values having significant impact on satisfaction were dialogue and transparency, whereas the impact of access and risk-assessment has been found insignificant in the study.
A possible reason behind the insignificant role of access on satisfaction may be the unavailability of the required information to users beforehand, which not only hampers the process of co-creation but also affects the trust between company and customers. Another aspect of finding assess as an insignificant component of co-creation may be the reluctance of companies in exposing price-related information to its customers, probably because of the revenue component associated with it. Although companies want their customers to participate in accomplishing the goal of developing an innovative product, these companies do not want their customers to participate in setting the prices, which in turn will be charged from them. Moreover, it is evident from the reviewed literature that a well-informed customer can actively participate in and help the company with co-creating an innovative and competitive product or service with all respects. One of the previous studies has also cited the similar results of the insignificant role of access in co-creation in the case of the telecommunications industry (Taghizadeh et al., 2016). Furthermore, fear of loss could be the possible reason behind the insignificance of risk-assessment in the present study. Companies could be under the impression that they will fail if they disclose the potential risk associated with the product/service to its customer.
The respondents further stated that they play a minimal role in co-creation, specifically the in case of e-resources, as most of the content is presented to them as per the perception of the company, not as per the requirement of customers. Very few platforms provide a section for feedback on their portals, showing little or no intention to improve their products or services. As a result, customers cannot act as co-creators in developing a good featured product or service. Rather, these customers remained unexposed to all the relevant information and risk connected with the offering of the company. Furthermore, this study elaborated and confirmed the impact of users’ satisfaction on their intentions to adopt e-resources with the help of significant results. Following such results, this study is proved to be a unique piece of work, associating value co-creation, satisfaction, and intentions of users to adopt e-resources into one frame. The collected responses have confirmed the association among all three variables and validated the proposed framework of the study.
7. IMPLICATIONS OF THE STUDY
With respect to previous research, the present investigation provides very important contributions for academicians, librarians, and e-resource providers. Value co-creation is a unique concept, being adopted by many industries for encouraging its customers/users to participate in the process of development of a new product or service. The DART model of value co-creation focuses on the same phenomena by highlighting the role of “dialogue, access, risk-assessment, and transparency” in co-creating values. It has been evident from available literature that all of these variables have been employed in many areas of research such as social media, education, government e-services, savings groups, travel services, and online retailing. However, e-resources is one of the unique areas which has been identified as untouched. Companies working on development of e-resources are still not getting benefits from the concept of co-creation. The present study intended to fulfil this gap and investigated the notion of value co-creation in the area of e-resources. Findings of the study will contribute to the existing school of knowledge and help e-resource providers in developing these resources by employing co-creation for the sake of comprehending the viewpoint and requirements of the users.
8. LIMITATIONS AND SCOPE FOR FUTURE RESEARCH
The current investigation carries some limitation, which creates opportunities for further exploration. To mention a few, the data has been gathered from India only, so it will be difficult to generalize results. Thus, future researchers may collect data from other countries and geographies to generalize the results. Moreover, future studies might qualitatively explore the reasons for the insignificant impact of access and risk-assessment. Future researchers may also target managers of e-resource companies to explore the motives of low customer involvement in co-creation of e-resources. The present study has taken all kinds of e-resources in consideration, so future studies may adopt a case-based research methodology by choosing specific kinds of e-resources for their study.
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